{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T03:39:15.192891Z",
     "start_time": "2021-06-29T03:39:15.178183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('3.6.9', '1.19.0', '3.2.2')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from dsutil.plotting import add_grid\n",
    "from platform import python_version\n",
    "\n",
    "python_version(), np.__version__,matplotlib.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comma as thousands separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:12:48.800860Z",
     "start_time": "2021-06-29T04:12:48.620283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "# generate sample data for this example\n",
    "xs = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "ys=np.random.normal(loc=10000,size=12, scale=20000.55) + 100000.05\n",
    "\n",
    "# plot the data\n",
    "plt.bar(xs,ys)\n",
    "\n",
    "# after plotting the data, format the labels\n",
    "current_values = plt.gca().get_yticks()\n",
    "plt.gca().set_yticklabels(['{:,.0f}'.format(x) for x in current_values])\n",
    "\n",
    "add_grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disable Scientific Notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:16:37.133575Z",
     "start_time": "2021-06-29T04:16:36.955663Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "# generate sample data for this example\n",
    "xs = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "ys=np.random.normal(loc=0,size=12, scale=500000) + 1000000\n",
    "\n",
    "# plot the data\n",
    "plt.bar(xs,ys)\n",
    "\n",
    "# after plotting the data, format the labels\n",
    "current_values = plt.gca().get_yticks()\n",
    "plt.gca().set_yticklabels(['{:.0f}'.format(x) for x in current_values])\n",
    "\n",
    "add_grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:13:16.096064Z",
     "start_time": "2021-06-29T04:13:16.091037Z"
    }
   },
   "source": [
    "## Format yaxis as percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:20:33.518433Z",
     "start_time": "2021-06-29T04:20:33.513160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.repeat(1.0, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:21:39.271077Z",
     "start_time": "2021-06-29T04:21:39.254912Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.21613195, 0.46872558, 1.        , 1.        ,\n",
       "       1.        , 0.95374069, 0.87955852, 0.4085912 , 0.71206232,\n",
       "       0.81574449, 1.        ])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.minimum(np.random.normal(loc=1,size=12,scale=0.4), \n",
    "           np.repeat(1.0, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-29T04:26:06.930965Z",
     "start_time": "2021-06-29T04:26:06.764922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "# generate sample data for this example\n",
    "xs = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "ys= np.minimum(np.random.normal(loc=0.5,size=12,scale=0.4), np.repeat(1.0, 12))\n",
    "\n",
    "# plot the data\n",
    "plt.bar(xs,ys)\n",
    "\n",
    "plt.ylim(0,1.05)\n",
    "\n",
    "# after plotting the data, format the labels\n",
    "current_values = plt.gca().get_yticks()\n",
    "plt.gca().set_yticklabels(['{:,.0%}'.format(x) for x in current_values])\n",
    "\n",
    "add_grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
